A decision support system for assisting fine needle aspiration diagnosis of thyroid malignancy.

Abstract

Objective: To assist diagnosis of thyroid malignancy, implementing a decision support system (DSS) using fine needle aspiration biopsy (FNAB) data.

Study design: The set of 2,035 thyroid smears contained 1,886 smears of nonmalignancy (class 1) and 150 smears of malignancy (class 2) verified histologically. For each smear, 67 medical features were considered by the expert, forming 2,036 feature vectors, which were fed into a DSS for discriminating between malignant and nonmalignant smears. The DSS comprised a feature selection and classification module using a combination of three classifiers, the artificial neural network, the support vector machines, and the k-nearest neighbor, under the majority vote procedure.

Results: The overall classification accuracy of the DSS was 98.6%, marginally better than the FNAB (97.3%). The DSS had lower sensitivity (89.1%) and better specificity (99.4%) compared to the FNAB. Regarding the smears characterized as "suspicious" by FNAB, a significant improvement of overall accuracy was obtained by the proposed DSS system (84.6%) compared to the FNAB (50.0%).

Conclusion: The proposed DSS provides significant improvement compared to FNAB regarding discrimination of smears characterized by an expert as "suspicious," reducing the number of patients undergoing surgical procedures.